residual networks. The proposed model is able to integrate domain knowledge and
researchers' understanding of the task by virtue of different neural network building blocks.
Specifically, a modified deep residual network is formulated to improve the forecast results.
Further, a two-stage ensemble strategy is used to enhance the generalization capability of
the proposed model. We also apply the proposed model to probabilistic load forecasting …